Overview

Dataset statistics

Number of variables45
Number of observations101763
Missing cells54020
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory184.6 MiB
Average record size in memory1.9 KiB

Variable types

CAT30
NUM13
BOOL2

Warnings

medical_specialty has a high cardinality: 72 distinct values High cardinality
race has 2271 (2.2%) missing values Missing
medical_specialty has 49947 (49.1%) missing values Missing
cat_diag_3 has 1423 (1.4%) missing values Missing
df_index has unique values Unique
num_procedures has 46652 (45.8%) zeros Zeros
number_outpatient has 85024 (83.6%) zeros Zeros
number_emergency has 90380 (88.8%) zeros Zeros
number_inpatient has 67627 (66.5%) zeros Zeros

Reproduction

Analysis started2020-09-14 01:40:28.301801
Analysis finished2020-09-14 01:41:27.116702
Duration58.81 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct101763
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50882.14641
Minimum0
Maximum101765
Zeros1
Zeros (%)< 0.1%
Memory size795.1 KiB
2020-09-13T20:41:27.249551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5088.1
Q125440.5
median50882
Q376323.5
95-th percentile96676.9
Maximum101765
Range101765
Interquartile range (IQR)50883

Descriptive statistics

Standard deviation29377.55192
Coefficient of variation (CV)0.5773646357
Kurtosis-1.1999904
Mean50882.14641
Median Absolute Deviation (MAD)25442
Skewness2.123409245e-05
Sum5177919865
Variance863040557
MonotocityStrictly increasing
2020-09-13T20:41:27.398442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
258941< 0.1%
 
54161< 0.1%
 
74651< 0.1%
 
13221< 0.1%
 
33711< 0.1%
 
136121< 0.1%
 
156611< 0.1%
 
95181< 0.1%
 
115671< 0.1%
 
545761< 0.1%
 
566251< 0.1%
 
504821< 0.1%
 
525311< 0.1%
 
627721< 0.1%
 
648211< 0.1%
 
586781< 0.1%
 
607271< 0.1%
 
382001< 0.1%
 
402491< 0.1%
 
341061< 0.1%
 
361551< 0.1%
 
463961< 0.1%
 
279431< 0.1%
 
320371< 0.1%
 
Other values (101738)101738> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
51< 0.1%
 
61< 0.1%
 
71< 0.1%
 
81< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
1017651< 0.1%
 
1017641< 0.1%
 
1017631< 0.1%
 
1017621< 0.1%
 
1017611< 0.1%
 
1017601< 0.1%
 
1017591< 0.1%
 
1017581< 0.1%
 
1017571< 0.1%
 
1017561< 0.1%
 

race
Categorical

MISSING

Distinct5
Distinct (%)< 0.1%
Missing2271
Missing (%)2.2%
Memory size795.1 KiB
Caucasian
76099 
AfricanAmerican
19210 
Hispanic
 
2037
Other
 
1505
Asian
 
641
ValueCountFrequency (%) 
Caucasian7609974.8%
 
AfricanAmerican1921018.9%
 
Hispanic20372.0%
 
Other15051.5%
 
Asian6410.6%
 
(Missing)22712.2%
 
2020-09-13T20:41:27.585404image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:27.737093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:27.892492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length9
Mean length9.894362391
Min length3

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a27166627.0%
 
n12173912.1%
 
i11923411.8%
 
c11655611.6%
 
s787777.8%
 
C760997.6%
 
u760997.6%
 
r399254.0%
 
A390613.9%
 
e207152.1%
 
f192101.9%
 
m192101.9%
 
H20370.2%
 
p20370.2%
 
O15050.1%
 
t15050.1%
 
h15050.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter88817888.2%
 
Uppercase Letter11870211.8%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C7609964.1%
 
A3906132.9%
 
H20371.7%
 
O15051.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a27166630.6%
 
n12173913.7%
 
i11923413.4%
 
c11655613.1%
 
s787778.9%
 
u760998.6%
 
r399254.5%
 
e207152.3%
 
f192102.2%
 
m192102.2%
 
p20370.2%
 
t15050.2%
 
h15050.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1006880100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a27166627.0%
 
n12173912.1%
 
i11923411.8%
 
c11655611.6%
 
s787777.8%
 
C760997.6%
 
u760997.6%
 
r399254.0%
 
A390613.9%
 
e207152.1%
 
f192101.9%
 
m192101.9%
 
H20370.2%
 
p20370.2%
 
O15050.1%
 
t15050.1%
 
h15050.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1006880100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a27166627.0%
 
n12173912.1%
 
i11923411.8%
 
c11655611.6%
 
s787777.8%
 
C760997.6%
 
u760997.6%
 
r399254.0%
 
A390613.9%
 
e207152.1%
 
f192101.9%
 
m192101.9%
 
H20370.2%
 
p20370.2%
 
O15050.1%
 
t15050.1%
 
h15050.1%
 

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
Female
54708 
Male
47055 
ValueCountFrequency (%) 
Female5470853.8%
 
Male4705546.2%
 
2020-09-13T20:41:28.008159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:28.094179image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:28.228248image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length5.075204151
Min length4

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e15647130.3%
 
a10176319.7%
 
l10176319.7%
 
F5470810.6%
 
m5470810.6%
 
M470559.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter41470580.3%
 
Uppercase Letter10176319.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F5470853.8%
 
M4705546.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e15647137.7%
 
a10176324.5%
 
l10176324.5%
 
m5470813.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin516468100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e15647130.3%
 
a10176319.7%
 
l10176319.7%
 
F5470810.6%
 
m5470810.6%
 
M470559.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII516468100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e15647130.3%
 
a10176319.7%
 
l10176319.7%
 
F5470810.6%
 
m5470810.6%
 
M470559.1%
 

age
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.167516591
Minimum1.791759469
Maximum4.564348191
Zeros0
Zeros (%)0.0%
Memory size795.1 KiB
2020-09-13T20:41:28.383055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.791759469
5-th percentile3.583518938
Q14.025351691
median4.189654742
Q34.33073334
95-th percentile4.454347296
Maximum4.564348191
Range2.772588722
Interquartile range (IQR)0.3053816496

Descriptive statistics

Standard deviation0.2962689476
Coefficient of variation (CV)0.07109004633
Kurtosis9.317491555
Mean4.167516591
Median Absolute Deviation (MAD)0.1643030513
Skewness-2.178596467
Sum424098.9908
Variance0.08777528928
MonotocityNot monotonic
2020-09-13T20:41:28.516110image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4.330733342606625.6%
 
4.1896547422248222.1%
 
4.0253516911725617.0%
 
4.4543472961719716.9%
 
3.82864139696859.5%
 
3.58351893837753.7%
 
4.56434819127932.7%
 
3.25809653816571.6%
 
2.7725887226910.7%
 
1.7917594691610.2%
 
ValueCountFrequency (%) 
1.7917594691610.2%
 
2.7725887226910.7%
 
3.25809653816571.6%
 
3.58351893837753.7%
 
3.82864139696859.5%
 
4.0253516911725617.0%
 
4.1896547422248222.1%
 
4.330733342606625.6%
 
4.4543472961719716.9%
 
4.56434819127932.7%
 
ValueCountFrequency (%) 
4.56434819127932.7%
 
4.4543472961719716.9%
 
4.330733342606625.6%
 
4.1896547422248222.1%
 
4.0253516911725617.0%
 
3.82864139696859.5%
 
3.58351893837753.7%
 
3.25809653816571.6%
 
2.7725887226910.7%
 
1.7917594691610.2%
 

admission_type_id
Real number (ℝ≥0)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.024016588
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size795.1 KiB
2020-09-13T20:41:28.655948image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.445413768
Coefficient of variation (CV)0.7141313846
Kurtosis1.942418805
Mean2.024016588
Median Absolute Deviation (MAD)0
Skewness1.591977215
Sum205970
Variance2.089220961
MonotocityNot monotonic
2020-09-13T20:41:28.790379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
15398853.1%
 
31886818.5%
 
21848018.2%
 
652915.2%
 
547854.7%
 
83200.3%
 
721< 0.1%
 
410< 0.1%
 
ValueCountFrequency (%) 
15398853.1%
 
21848018.2%
 
31886818.5%
 
410< 0.1%
 
547854.7%
 
652915.2%
 
721< 0.1%
 
83200.3%
 
ValueCountFrequency (%) 
83200.3%
 
721< 0.1%
 
652915.2%
 
547854.7%
 
410< 0.1%
 
31886818.5%
 
21848018.2%
 
15398853.1%
 

discharge_disposition_id
Real number (ℝ≥0)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.715515462
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Memory size795.1 KiB
2020-09-13T20:41:28.935788image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.279918511
Coefficient of variation (CV)1.421046034
Kurtosis6.004127661
Mean3.715515462
Median Absolute Deviation (MAD)0
Skewness2.563168617
Sum378102
Variance27.87753948
MonotocityNot monotonic
2020-09-13T20:41:29.094555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
16023259.2%
 
31395413.7%
 
61290212.7%
 
1836913.6%
 
221282.1%
 
2219922.0%
 
1116421.6%
 
511841.2%
 
259891.0%
 
48150.8%
 
76230.6%
 
234120.4%
 
133990.4%
 
143720.4%
 
281390.1%
 
81080.1%
 
15630.1%
 
2448< 0.1%
 
921< 0.1%
 
1714< 0.1%
 
1611< 0.1%
 
198< 0.1%
 
106< 0.1%
 
275< 0.1%
 
123< 0.1%
 
ValueCountFrequency (%) 
16023259.2%
 
221282.1%
 
31395413.7%
 
48150.8%
 
511841.2%
 
61290212.7%
 
76230.6%
 
81080.1%
 
921< 0.1%
 
106< 0.1%
 
ValueCountFrequency (%) 
281390.1%
 
275< 0.1%
 
259891.0%
 
2448< 0.1%
 
234120.4%
 
2219922.0%
 
202< 0.1%
 
198< 0.1%
 
1836913.6%
 
1714< 0.1%
 

admission_source_id
Real number (ℝ≥0)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.75445889
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Memory size795.1 KiB
2020-09-13T20:41:29.296470image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.06410966
Coefficient of variation (CV)0.7062540089
Kurtosis1.744953515
Mean5.75445889
Median Absolute Deviation (MAD)0
Skewness1.029942076
Sum585591
Variance16.51698733
MonotocityNot monotonic
2020-09-13T20:41:29.460505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
75749256.5%
 
12956429.1%
 
1767816.7%
 
431873.1%
 
622642.2%
 
211041.1%
 
58550.8%
 
31870.2%
 
201610.2%
 
91250.1%
 
816< 0.1%
 
2212< 0.1%
 
108< 0.1%
 
112< 0.1%
 
142< 0.1%
 
252< 0.1%
 
131< 0.1%
 
ValueCountFrequency (%) 
12956429.1%
 
211041.1%
 
31870.2%
 
431873.1%
 
58550.8%
 
622642.2%
 
75749256.5%
 
816< 0.1%
 
91250.1%
 
108< 0.1%
 
ValueCountFrequency (%) 
252< 0.1%
 
2212< 0.1%
 
201610.2%
 
1767816.7%
 
142< 0.1%
 
131< 0.1%
 
112< 0.1%
 
108< 0.1%
 
91250.1%
 
816< 0.1%
 

time_in_hospital
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.542969309
Minimum0.6931471806
Maximum2.708050201
Zeros0
Zeros (%)0.0%
Memory size795.1 KiB
2020-09-13T20:41:29.652118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.6931471806
5-th percentile0.6931471806
Q11.098612289
median1.609437912
Q31.945910149
95-th percentile2.48490665
Maximum2.708050201
Range2.014903021
Interquartile range (IQR)0.8472978604

Descriptive statistics

Standard deviation0.5341506091
Coefficient of variation (CV)0.3461835604
Kurtosis-0.7576336978
Mean1.542969309
Median Absolute Deviation (MAD)0.4700036292
Skewness0.1033598842
Sum157017.1858
Variance0.2853168732
MonotocityNot monotonic
2020-09-13T20:41:29.830290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
1.3862943611775617.4%
 
1.0986122891722416.9%
 
0.69314718061420614.0%
 
1.6094379121392413.7%
 
1.79175946999669.8%
 
1.94591014975397.4%
 
2.07944154258595.8%
 
2.19722457743904.3%
 
2.30258509330022.9%
 
2.39789527323422.3%
 
2.4849066518551.8%
 
2.56494935714481.4%
 
2.6390573312101.2%
 
2.70805020110421.0%
 
ValueCountFrequency (%) 
0.69314718061420614.0%
 
1.0986122891722416.9%
 
1.3862943611775617.4%
 
1.6094379121392413.7%
 
1.79175946999669.8%
 
1.94591014975397.4%
 
2.07944154258595.8%
 
2.19722457743904.3%
 
2.30258509330022.9%
 
2.39789527323422.3%
 
ValueCountFrequency (%) 
2.70805020110421.0%
 
2.6390573312101.2%
 
2.56494935714481.4%
 
2.4849066518551.8%
 
2.39789527323422.3%
 
2.30258509330022.9%
 
2.19722457743904.3%
 
2.07944154258595.8%
 
1.94591014975397.4%
 
1.79175946999669.8%
 

medical_specialty
Categorical

HIGH CARDINALITY
MISSING

Distinct72
Distinct (%)0.1%
Missing49947
Missing (%)49.1%
Memory size795.1 KiB
InternalMedicine
14635 
Emergency/Trauma
7565 
Family/GeneralPractice
7440 
Cardiology
5351 
Surgery-General
3099 
Other values (67)
13726 
ValueCountFrequency (%) 
InternalMedicine1463514.4%
 
Emergency/Trauma75657.4%
 
Family/GeneralPractice74407.3%
 
Cardiology53515.3%
 
Surgery-General30993.0%
 
Nephrology16131.6%
 
Orthopedics14001.4%
 
Orthopedics-Reconstructive12331.2%
 
Radiologist11401.1%
 
Pulmonology8710.9%
 
Psychiatry8540.8%
 
Urology6850.7%
 
ObstetricsandGynecology6710.7%
 
Surgery-Cardiovascular/Thoracic6520.6%
 
Gastroenterology5640.6%
 
Surgery-Vascular5330.5%
 
Surgery-Neuro4680.5%
 
PhysicalMedicineandRehabilitation3910.4%
 
Oncology3480.3%
 
Pediatrics2540.2%
 
Hematology/Oncology2070.2%
 
Neurology2030.2%
 
Pediatrics-Endocrinology1590.2%
 
Otolaryngology1250.1%
 
Endocrinology1200.1%
 
Other values (47)12351.2%
 
(Missing)4994749.1%
 
2020-09-13T20:41:30.098085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)< 0.1%
2020-09-13T20:41:30.285663image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length8
Mean length9.594440022
Min length3

Overview of Unicode Properties

Unique unicode characters43
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n16869217.3%
 
a12109512.4%
 
e10515110.8%
 
r768987.9%
 
i633076.5%
 
c500075.1%
 
l488705.0%
 
y349363.6%
 
t341493.5%
 
o340513.5%
 
d270342.8%
 
g255952.6%
 
m238462.4%
 
u168561.7%
 
/158711.6%
 
M150551.5%
 
I146831.5%
 
G118821.2%
 
s106381.1%
 
P104481.1%
 
T83320.9%
 
E78610.8%
 
F74510.8%
 
h69650.7%
 
-66270.7%
 
Other values (18)300593.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter85567887.6%
 
Uppercase Letter9814710.1%
 
Other Punctuation159071.6%
 
Dash Punctuation66270.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M1505515.3%
 
I1468315.0%
 
G1188212.1%
 
P1044810.6%
 
T83328.5%
 
E78618.0%
 
F74517.6%
 
C63066.4%
 
S51565.3%
 
O41464.2%
 
R28472.9%
 
N23072.4%
 
U6850.7%
 
V5330.5%
 
H3510.4%
 
A550.1%
 
D49< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n16869219.7%
 
a12109514.2%
 
e10515112.3%
 
r768989.0%
 
i633077.4%
 
c500075.8%
 
l488705.7%
 
y349364.1%
 
t341494.0%
 
o340514.0%
 
d270343.2%
 
g255953.0%
 
m238462.8%
 
u168562.0%
 
s106381.2%
 
h69650.8%
 
p44160.5%
 
v19960.2%
 
b11140.1%
 
f49< 0.1%
 
x11< 0.1%
 
w1< 0.1%
 
k1< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-6627100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/1587199.8%
 
&360.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin95382597.7%
 
Common225342.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n16869217.7%
 
a12109512.7%
 
e10515111.0%
 
r768988.1%
 
i633076.6%
 
c500075.2%
 
l488705.1%
 
y349363.7%
 
t341493.6%
 
o340513.6%
 
d270342.8%
 
g255952.7%
 
m238462.5%
 
u168561.8%
 
M150551.6%
 
I146831.5%
 
G118821.2%
 
s106381.1%
 
P104481.1%
 
T83320.9%
 
E78610.8%
 
F74510.8%
 
h69650.7%
 
C63060.7%
 
S51560.5%
 
Other values (15)185611.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
/1587170.4%
 
-662729.4%
 
&360.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII976359100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n16869217.3%
 
a12109512.4%
 
e10515110.8%
 
r768987.9%
 
i633076.5%
 
c500075.1%
 
l488705.0%
 
y349363.6%
 
t341493.5%
 
o340513.5%
 
d270342.8%
 
g255952.6%
 
m238462.4%
 
u168561.7%
 
/158711.6%
 
M150551.5%
 
I146831.5%
 
G118821.2%
 
s106381.1%
 
P104481.1%
 
T83320.9%
 
E78610.8%
 
F74510.8%
 
h69650.7%
 
-66270.7%
 
Other values (18)300593.1%
 

num_lab_procedures
Real number (ℝ≥0)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.594660782
Minimum0.6931471806
Maximum4.890349128
Zeros0
Zeros (%)0.0%
Memory size795.1 KiB
2020-09-13T20:41:30.414463image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.6931471806
5-th percentile1.609437912
Q13.465735903
median3.80666249
Q34.060443011
95-th percentile4.304065093
Maximum4.890349128
Range4.197201948
Interquartile range (IQR)0.5947071077

Descriptive statistics

Standard deviation0.7863905162
Coefficient of variation (CV)0.2187662658
Kurtosis5.034180759
Mean3.594660782
Median Absolute Deviation (MAD)0.2708749541
Skewness-2.222758425
Sum365803.4652
Variance0.6184100439
MonotocityNot monotonic
2020-09-13T20:41:30.580964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.693147180632083.2%
 
3.78418963428042.8%
 
3.8066624924962.5%
 
3.82864139623762.3%
 
3.66356164622122.2%
 
3.71357206722012.2%
 
3.85014760221892.2%
 
3.73766961821172.1%
 
3.76120011621132.1%
 
3.87120101121062.1%
 
3.68887945421012.1%
 
3.6375861620792.0%
 
3.91202300520662.0%
 
3.89182029820582.0%
 
3.61091791319621.9%
 
3.95124371919251.9%
 
3.93182563319241.9%
 
3.58351893819071.9%
 
4.00733318518881.9%
 
4.04305126818391.8%
 
3.97029191418381.8%
 
4.02535169118361.8%
 
3.98898404718021.8%
 
4.06044301117471.7%
 
4.07753744417081.7%
 
Other values (93)4926148.4%
 
ValueCountFrequency (%) 
0.693147180632083.2%
 
1.09861228911011.1%
 
1.3862943616680.7%
 
1.6094379123780.4%
 
1.7917594692850.3%
 
1.9459101492820.3%
 
2.0794415423230.3%
 
2.1972245773660.4%
 
2.3025850939330.9%
 
2.3978952738380.8%
 
ValueCountFrequency (%) 
4.8903491281< 0.1%
 
4.867534451< 0.1%
 
4.8441870861< 0.1%
 
4.8040210451< 0.1%
 
4.7957905461< 0.1%
 
4.7791234931< 0.1%
 
4.7449321282< 0.1%
 
4.7361984483< 0.1%
 
4.7184988713< 0.1%
 
4.7004803664< 0.1%
 

num_procedures
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6222107291
Minimum0
Maximum1.945910149
Zeros46652
Zeros (%)45.8%
Memory size795.1 KiB
2020-09-13T20:41:30.754617image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.6931471806
Q31.098612289
95-th percentile1.791759469
Maximum1.945910149
Range1.945910149
Interquartile range (IQR)1.098612289

Descriptive statistics

Standard deviation0.654017933
Coefficient of variation (CV)1.051119665
Kurtosis-1.076967217
Mean0.6222107291
Median Absolute Deviation (MAD)0.6931471806
Skewness0.519590453
Sum63318.03043
Variance0.4277394567
MonotocityNot monotonic
2020-09-13T20:41:30.891843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
04665245.8%
 
0.69314718062074120.4%
 
1.0986122891271612.5%
 
1.38629436194439.3%
 
1.94591014949544.9%
 
1.60943791241804.1%
 
1.79175946930773.0%
 
ValueCountFrequency (%) 
04665245.8%
 
0.69314718062074120.4%
 
1.0986122891271612.5%
 
1.38629436194439.3%
 
1.60943791241804.1%
 
1.79175946930773.0%
 
1.94591014949544.9%
 
ValueCountFrequency (%) 
1.94591014949544.9%
 
1.79175946930773.0%
 
1.60943791241804.1%
 
1.38629436194439.3%
 
1.0986122891271612.5%
 
0.69314718062074120.4%
 
04665245.8%
 

num_medications
Real number (ℝ≥0)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.722372617
Minimum0.6931471806
Maximum4.406719247
Zeros0
Zeros (%)0.0%
Memory size795.1 KiB
2020-09-13T20:41:31.063949image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.6931471806
5-th percentile1.945910149
Q12.397895273
median2.772588722
Q33.044522438
95-th percentile3.465735903
Maximum4.406719247
Range3.713572067
Interquartile range (IQR)0.6466271649

Descriptive statistics

Standard deviation0.4892828957
Coefficient of variation (CV)0.1797266446
Kurtosis0.9100512756
Mean2.722372617
Median Absolute Deviation (MAD)0.2876820725
Skewness-0.4853188019
Sum277036.8046
Variance0.239397752
MonotocityNot monotonic
2020-09-13T20:41:31.238054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.6390573360866.0%
 
2.56494935760045.9%
 
2.4849066557955.7%
 
2.77258872257925.7%
 
2.70805020157075.6%
 
2.83321334454305.3%
 
2.39789527353465.3%
 
2.89037175849194.8%
 
2.30258509349134.8%
 
2.94443897945234.4%
 
2.19722457743534.3%
 
2.99573227440784.0%
 
3.04452243836913.6%
 
2.07944154234843.4%
 
3.09104245332293.2%
 
3.13549421628672.8%
 
1.94591014926982.7%
 
3.1780538324262.4%
 
3.21887582521092.1%
 
1.79175946920172.0%
 
3.25809653818881.9%
 
3.29583686616081.6%
 
3.3322045114321.4%
 
1.60943791214171.4%
 
3.3672958312331.2%
 
Other values (50)87188.6%
 
ValueCountFrequency (%) 
0.69314718062620.3%
 
1.0986122894700.5%
 
1.3862943619000.9%
 
1.60943791214171.4%
 
1.79175946920172.0%
 
1.94591014926982.7%
 
2.07944154234843.4%
 
2.19722457743534.3%
 
2.30258509349134.8%
 
2.39789527353465.3%
 
ValueCountFrequency (%) 
4.4067192471< 0.1%
 
4.3820266351< 0.1%
 
4.330733342< 0.1%
 
4.3174881141< 0.1%
 
4.2904594413< 0.1%
 
4.2626798772< 0.1%
 
4.2484952425< 0.1%
 
4.2341065057< 0.1%
 
4.2195077057< 0.1%
 
4.2046926195< 0.1%
 

number_outpatient
Real number (ℝ≥0)

ZEROS

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.172140067
Minimum0
Maximum3.761200116
Zeros85024
Zeros (%)83.6%
Memory size795.1 KiB
2020-09-13T20:41:31.418393image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.098612289
Maximum3.761200116
Range3.761200116
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.429351371
Coefficient of variation (CV)2.494197768
Kurtosis7.851673556
Mean0.172140067
Median Absolute Deviation (MAD)0
Skewness2.739685741
Sum17517.48964
Variance0.1843425998
MonotocityNot monotonic
2020-09-13T20:41:31.588405image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
08502483.6%
 
0.693147180685478.4%
 
1.09861228935943.5%
 
1.38629436120422.0%
 
1.60943791210991.1%
 
1.7917594695330.5%
 
1.9459101493030.3%
 
2.0794415421550.2%
 
2.197224577980.1%
 
2.302585093830.1%
 
2.397895273570.1%
 
2.4849066542< 0.1%
 
2.6390573331< 0.1%
 
2.56494935730< 0.1%
 
2.70805020128< 0.1%
 
2.77258872220< 0.1%
 
2.83321334415< 0.1%
 
2.8903717588< 0.1%
 
3.0445224387< 0.1%
 
3.0910424537< 0.1%
 
3.1354942165< 0.1%
 
2.9444389795< 0.1%
 
3.332204513< 0.1%
 
3.2188758253< 0.1%
 
2.9957322743< 0.1%
 
Other values (14)21< 0.1%
 
ValueCountFrequency (%) 
08502483.6%
 
0.693147180685478.4%
 
1.09861228935943.5%
 
1.38629436120422.0%
 
1.60943791210991.1%
 
1.7917594695330.5%
 
1.9459101493030.3%
 
2.0794415421550.2%
 
2.197224577980.1%
 
2.302585093830.1%
 
ValueCountFrequency (%) 
3.7612001161< 0.1%
 
3.7135720671< 0.1%
 
3.6888794541< 0.1%
 
3.6635616461< 0.1%
 
3.637586161< 0.1%
 
3.6109179132< 0.1%
 
3.5835189382< 0.1%
 
3.5553480611< 0.1%
 
3.5263605252< 0.1%
 
3.4011973822< 0.1%
 

number_emergency
Real number (ℝ≥0)

ZEROS

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1019147643
Minimum0
Maximum4.343805422
Zeros90380
Zeros (%)88.8%
Memory size795.1 KiB
2020-09-13T20:41:31.773270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.6931471806
Maximum4.343805422
Range4.343805422
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3149687265
Coefficient of variation (CV)3.090511259
Kurtosis16.20219531
Mean0.1019147643
Median Absolute Deviation (MAD)0
Skewness3.653584959
Sum10371.15216
Variance0.09920529867
MonotocityNot monotonic
2020-09-13T20:41:31.949725image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%) 
09038088.8%
 
0.693147180676777.5%
 
1.09861228920422.0%
 
1.3862943617250.7%
 
1.6094379123740.4%
 
1.7917594691920.2%
 
1.945910149940.1%
 
2.079441542730.1%
 
2.19722457750< 0.1%
 
2.39789527334< 0.1%
 
2.30258509333< 0.1%
 
2.4849066523< 0.1%
 
2.6390573312< 0.1%
 
2.56494935710< 0.1%
 
3.1354942166< 0.1%
 
2.8332133445< 0.1%
 
2.9444389795< 0.1%
 
3.0445224384< 0.1%
 
2.9957322744< 0.1%
 
2.7080502013< 0.1%
 
2.7725887223< 0.1%
 
3.0910424532< 0.1%
 
3.2580965382< 0.1%
 
3.7612001161< 0.1%
 
3.8501476021< 0.1%
 
Other values (8)8< 0.1%
 
ValueCountFrequency (%) 
09038088.8%
 
0.693147180676777.5%
 
1.09861228920422.0%
 
1.3862943617250.7%
 
1.6094379123740.4%
 
1.7917594691920.2%
 
1.945910149940.1%
 
2.079441542730.1%
 
2.19722457750< 0.1%
 
2.30258509333< 0.1%
 
ValueCountFrequency (%) 
4.3438054221< 0.1%
 
4.174387271< 0.1%
 
4.1588830831< 0.1%
 
4.0073331851< 0.1%
 
3.8501476021< 0.1%
 
3.7612001161< 0.1%
 
3.637586161< 0.1%
 
3.4011973821< 0.1%
 
3.367295831< 0.1%
 
3.2580965382< 0.1%
 

number_inpatient
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3263245056
Minimum0
Maximum3.091042453
Zeros67627
Zeros (%)66.5%
Memory size795.1 KiB
2020-09-13T20:41:32.138354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.6931471806
95-th percentile1.386294361
Maximum3.091042453
Range3.091042453
Interquartile range (IQR)0.6931471806

Descriptive statistics

Standard deviation0.5111057928
Coefficient of variation (CV)1.56625011
Kurtosis1.375074286
Mean0.3263245056
Median Absolute Deviation (MAD)0
Skewness1.441916818
Sum33207.76066
Variance0.2612291314
MonotocityNot monotonic
2020-09-13T20:41:32.319154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
06762766.5%
 
0.69314718061952119.2%
 
1.09861228975667.4%
 
1.38629436134113.4%
 
1.60943791216221.6%
 
1.7917594698120.8%
 
1.9459101494800.5%
 
2.0794415422680.3%
 
2.1972245771510.1%
 
2.3025850931110.1%
 
2.397895273610.1%
 
2.4849066549< 0.1%
 
2.56494935734< 0.1%
 
2.6390573320< 0.1%
 
2.70805020110< 0.1%
 
2.7725887229< 0.1%
 
2.8332133446< 0.1%
 
2.9957322742< 0.1%
 
2.8903717581< 0.1%
 
2.9444389791< 0.1%
 
3.0910424531< 0.1%
 
ValueCountFrequency (%) 
06762766.5%
 
0.69314718061952119.2%
 
1.09861228975667.4%
 
1.38629436134113.4%
 
1.60943791216221.6%
 
1.7917594698120.8%
 
1.9459101494800.5%
 
2.0794415422680.3%
 
2.1972245771510.1%
 
2.3025850931110.1%
 
ValueCountFrequency (%) 
3.0910424531< 0.1%
 
2.9957322742< 0.1%
 
2.9444389791< 0.1%
 
2.8903717581< 0.1%
 
2.8332133446< 0.1%
 
2.7725887229< 0.1%
 
2.70805020110< 0.1%
 
2.6390573320< 0.1%
 
2.56494935734< 0.1%
 
2.4849066549< 0.1%
 

number_diagnoses
Real number (ℝ≥0)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.422648703
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Memory size795.1 KiB
2020-09-13T20:41:32.483497image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.933577643
Coefficient of variation (CV)0.2604969897
Kurtosis-0.07888290487
Mean7.422648703
Median Absolute Deviation (MAD)1
Skewness-0.876799273
Sum755351
Variance3.738722502
MonotocityNot monotonic
2020-09-13T20:41:32.649714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
94947348.6%
 
51139211.2%
 
81061610.4%
 
71039310.2%
 
61016110.0%
 
455365.4%
 
328352.8%
 
210231.0%
 
12190.2%
 
1645< 0.1%
 
1017< 0.1%
 
1316< 0.1%
 
1111< 0.1%
 
1510< 0.1%
 
129< 0.1%
 
147< 0.1%
 
ValueCountFrequency (%) 
12190.2%
 
210231.0%
 
328352.8%
 
455365.4%
 
51139211.2%
 
61016110.0%
 
71039310.2%
 
81061610.4%
 
94947348.6%
 
1017< 0.1%
 
ValueCountFrequency (%) 
1645< 0.1%
 
1510< 0.1%
 
147< 0.1%
 
1316< 0.1%
 
129< 0.1%
 
1111< 0.1%
 
1017< 0.1%
 
94947348.6%
 
81061610.4%
 
71039310.2%
 

max_glu_serum
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
None
96417 
Norm
 
2597
>200
 
1485
>300
 
1264
ValueCountFrequency (%) 
None9641794.7%
 
Norm25972.6%
 
>20014851.5%
 
>30012641.2%
 
2020-09-13T20:41:32.839640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:32.974528image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:33.071296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N9901424.3%
 
o9901424.3%
 
n9641723.7%
 
e9641723.7%
 
054981.4%
 
>27490.7%
 
r25970.6%
 
m25970.6%
 
214850.4%
 
312640.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter29704273.0%
 
Uppercase Letter9901424.3%
 
Decimal Number82472.0%
 
Math Symbol27490.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N99014100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9901433.3%
 
n9641732.5%
 
e9641732.5%
 
r25970.9%
 
m25970.9%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>2749100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0549866.7%
 
2148518.0%
 
3126415.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin39605697.3%
 
Common109962.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N9901425.0%
 
o9901425.0%
 
n9641724.3%
 
e9641724.3%
 
r25970.7%
 
m25970.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
0549850.0%
 
>274925.0%
 
2148513.5%
 
3126411.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII407052100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N9901424.3%
 
o9901424.3%
 
n9641723.7%
 
e9641723.7%
 
054981.4%
 
>27490.7%
 
r25970.6%
 
m25970.6%
 
214850.4%
 
312640.3%
 

A1Cresult
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
None
84745 
>8
 
8216
Norm
 
4990
>7
 
3812
ValueCountFrequency (%) 
None8474583.3%
 
>882168.1%
 
Norm49904.9%
 
>738123.7%
 
2020-09-13T20:41:33.238355image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:33.377944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:33.493838image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length3.763607598
Min length2

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N8973523.4%
 
o8973523.4%
 
n8474522.1%
 
e8474522.1%
 
>120283.1%
 
882162.1%
 
r49901.3%
 
m49901.3%
 
738121.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter26920570.3%
 
Uppercase Letter8973523.4%
 
Math Symbol120283.1%
 
Decimal Number120283.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N89735100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o8973533.3%
 
n8474531.5%
 
e8474531.5%
 
r49901.9%
 
m49901.9%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>12028100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
8821668.3%
 
7381231.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin35894093.7%
 
Common240566.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N8973525.0%
 
o8973525.0%
 
n8474523.6%
 
e8474523.6%
 
r49901.4%
 
m49901.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
>1202850.0%
 
8821634.2%
 
7381215.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII382996100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N8973523.4%
 
o8973523.4%
 
n8474522.1%
 
e8474522.1%
 
>120283.1%
 
882162.1%
 
r49901.3%
 
m49901.3%
 
738121.0%
 

metformin
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
81776 
Steady
18345 
Up
 
1067
Down
 
575
ValueCountFrequency (%) 
No8177680.4%
 
Steady1834518.0%
 
Up10671.0%
 
Down5750.6%
 
2020-09-13T20:41:33.663830image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:33.801906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:33.912856image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.732388
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o8235129.6%
 
N8177629.4%
 
S183456.6%
 
t183456.6%
 
e183456.6%
 
a183456.6%
 
d183456.6%
 
y183456.6%
 
U10670.4%
 
p10670.4%
 
D5750.2%
 
w5750.2%
 
n5750.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter17629363.4%
 
Uppercase Letter10176336.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N8177680.4%
 
S1834518.0%
 
U10671.0%
 
D5750.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o8235146.7%
 
t1834510.4%
 
e1834510.4%
 
a1834510.4%
 
d1834510.4%
 
y1834510.4%
 
p10670.6%
 
w5750.3%
 
n5750.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin278056100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o8235129.6%
 
N8177629.4%
 
S183456.6%
 
t183456.6%
 
e183456.6%
 
a183456.6%
 
d183456.6%
 
y183456.6%
 
U10670.4%
 
p10670.4%
 
D5750.2%
 
w5750.2%
 
n5750.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII278056100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o8235129.6%
 
N8177629.4%
 
S183456.6%
 
t183456.6%
 
e183456.6%
 
a183456.6%
 
d183456.6%
 
y183456.6%
 
U10670.4%
 
p10670.4%
 
D5750.2%
 
w5750.2%
 
n5750.2%
 

repaglinide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
100224 
Steady
 
1384
Up
 
110
Down
 
45
ValueCountFrequency (%) 
No10022498.5%
 
Steady13841.4%
 
Up1100.1%
 
Down45< 0.1%
 
2020-09-13T20:41:34.085295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:34.219521image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:34.387802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.05528532
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10026947.9%
 
N10022447.9%
 
S13840.7%
 
t13840.7%
 
e13840.7%
 
a13840.7%
 
d13840.7%
 
y13840.7%
 
U1100.1%
 
p1100.1%
 
D45< 0.1%
 
w45< 0.1%
 
n45< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10738951.3%
 
Uppercase Letter10176348.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10022498.5%
 
S13841.4%
 
U1100.1%
 
D45< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10026993.4%
 
t13841.3%
 
e13841.3%
 
a13841.3%
 
d13841.3%
 
y13841.3%
 
p1100.1%
 
w45< 0.1%
 
n45< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin209152100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10026947.9%
 
N10022447.9%
 
S13840.7%
 
t13840.7%
 
e13840.7%
 
a13840.7%
 
d13840.7%
 
y13840.7%
 
U1100.1%
 
p1100.1%
 
D45< 0.1%
 
w45< 0.1%
 
n45< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII209152100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10026947.9%
 
N10022447.9%
 
S13840.7%
 
t13840.7%
 
e13840.7%
 
a13840.7%
 
d13840.7%
 
y13840.7%
 
U1100.1%
 
p1100.1%
 
D45< 0.1%
 
w45< 0.1%
 
n45< 0.1%
 

nateglinide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101060 
Steady
 
668
Up
 
24
Down
 
11
ValueCountFrequency (%) 
No10106099.3%
 
Steady6680.7%
 
Up24< 0.1%
 
Down11< 0.1%
 
2020-09-13T20:41:34.542793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:34.669284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:34.779848image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.026473276
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10107149.0%
 
N10106049.0%
 
S6680.3%
 
t6680.3%
 
e6680.3%
 
a6680.3%
 
d6680.3%
 
y6680.3%
 
U24< 0.1%
 
p24< 0.1%
 
D11< 0.1%
 
w11< 0.1%
 
n11< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10445750.7%
 
Uppercase Letter10176349.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10106099.3%
 
S6680.7%
 
U24< 0.1%
 
D11< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10107196.8%
 
t6680.6%
 
e6680.6%
 
a6680.6%
 
d6680.6%
 
y6680.6%
 
p24< 0.1%
 
w11< 0.1%
 
n11< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin206220100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10107149.0%
 
N10106049.0%
 
S6680.3%
 
t6680.3%
 
e6680.3%
 
a6680.3%
 
d6680.3%
 
y6680.3%
 
U24< 0.1%
 
p24< 0.1%
 
D11< 0.1%
 
w11< 0.1%
 
n11< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII206220100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10107149.0%
 
N10106049.0%
 
S6680.3%
 
t6680.3%
 
e6680.3%
 
a6680.3%
 
d6680.3%
 
y6680.3%
 
U24< 0.1%
 
p24< 0.1%
 
D11< 0.1%
 
w11< 0.1%
 
n11< 0.1%
 

chlorpropamide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101677 
Steady
 
79
Up
 
6
Down
 
1
ValueCountFrequency (%) 
No10167799.9%
 
Steady790.1%
 
Up6< 0.1%
 
Down1< 0.1%
 
2020-09-13T20:41:34.955732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-13T20:41:35.059811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:35.480173image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.003124908
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10167849.9%
 
N10167749.9%
 
S79< 0.1%
 
t79< 0.1%
 
e79< 0.1%
 
a79< 0.1%
 
d79< 0.1%
 
y79< 0.1%
 
U6< 0.1%
 
p6< 0.1%
 
D1< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10208150.1%
 
Uppercase Letter10176349.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10167799.9%
 
S790.1%
 
U6< 0.1%
 
D1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10167899.6%
 
t790.1%
 
e790.1%
 
a790.1%
 
d790.1%
 
y790.1%
 
p6< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203844100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10167849.9%
 
N10167749.9%
 
S79< 0.1%
 
t79< 0.1%
 
e79< 0.1%
 
a79< 0.1%
 
d79< 0.1%
 
y79< 0.1%
 
U6< 0.1%
 
p6< 0.1%
 
D1< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203844100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10167849.9%
 
N10167749.9%
 
S79< 0.1%
 
t79< 0.1%
 
e79< 0.1%
 
a79< 0.1%
 
d79< 0.1%
 
y79< 0.1%
 
U6< 0.1%
 
p6< 0.1%
 
D1< 0.1%
 
w1< 0.1%
 
n1< 0.1%
 

glimepiride
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
96572 
Steady
 
4670
Up
 
327
Down
 
194
ValueCountFrequency (%) 
No9657294.9%
 
Steady46704.6%
 
Up3270.3%
 
Down1940.2%
 
2020-09-13T20:41:35.649808image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:35.771214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:35.871752image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.187376551
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o9676643.5%
 
N9657243.4%
 
S46702.1%
 
t46702.1%
 
e46702.1%
 
a46702.1%
 
d46702.1%
 
y46702.1%
 
U3270.1%
 
p3270.1%
 
D1940.1%
 
w1940.1%
 
n1940.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter12083154.3%
 
Uppercase Letter10176345.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N9657294.9%
 
S46704.6%
 
U3270.3%
 
D1940.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9676680.1%
 
t46703.9%
 
e46703.9%
 
a46703.9%
 
d46703.9%
 
y46703.9%
 
p3270.3%
 
w1940.2%
 
n1940.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin222594100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o9676643.5%
 
N9657243.4%
 
S46702.1%
 
t46702.1%
 
e46702.1%
 
a46702.1%
 
d46702.1%
 
y46702.1%
 
U3270.1%
 
p3270.1%
 
D1940.1%
 
w1940.1%
 
n1940.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII222594100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o9676643.5%
 
N9657243.4%
 
S46702.1%
 
t46702.1%
 
e46702.1%
 
a46702.1%
 
d46702.1%
 
y46702.1%
 
U3270.1%
 
p3270.1%
 
D1940.1%
 
w1940.1%
 
n1940.1%
 

acetohexamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101762 
Steady
 
1
ValueCountFrequency (%) 
No101762> 99.9%
 
Steady1< 0.1%
 
2020-09-13T20:41:36.032642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-13T20:41:36.148710image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:36.233375image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000039307
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176250.0%
 
o10176250.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10176750.0%
 
Uppercase Letter10176350.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101762> 99.9%
 
S1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101762> 99.9%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203530100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176250.0%
 
o10176250.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203530100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176250.0%
 
o10176250.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

glipizide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
89078 
Steady
11355 
Up
 
770
Down
 
560
ValueCountFrequency (%) 
No8907887.5%
 
Steady1135511.2%
 
Up7700.8%
 
Down5600.6%
 
2020-09-13T20:41:36.445403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:36.588169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:36.758819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.457337146
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o8963835.8%
 
N8907835.6%
 
S113554.5%
 
t113554.5%
 
e113554.5%
 
a113554.5%
 
d113554.5%
 
y113554.5%
 
U7700.3%
 
p7700.3%
 
D5600.2%
 
w5600.2%
 
n5600.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter14830359.3%
 
Uppercase Letter10176340.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N8907887.5%
 
S1135511.2%
 
U7700.8%
 
D5600.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o8963860.4%
 
t113557.7%
 
e113557.7%
 
a113557.7%
 
d113557.7%
 
y113557.7%
 
p7700.5%
 
w5600.4%
 
n5600.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin250066100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o8963835.8%
 
N8907835.6%
 
S113554.5%
 
t113554.5%
 
e113554.5%
 
a113554.5%
 
d113554.5%
 
y113554.5%
 
U7700.3%
 
p7700.3%
 
D5600.2%
 
w5600.2%
 
n5600.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII250066100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o8963835.8%
 
N8907835.6%
 
S113554.5%
 
t113554.5%
 
e113554.5%
 
a113554.5%
 
d113554.5%
 
y113554.5%
 
U7700.3%
 
p7700.3%
 
D5600.2%
 
w5600.2%
 
n5600.2%
 

glyburide
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
91113 
Steady
9274 
Up
 
812
Down
 
564
ValueCountFrequency (%) 
No9111389.5%
 
Steady92749.1%
 
Up8120.8%
 
Down5640.6%
 
2020-09-13T20:41:36.940427image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:37.075382image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:37.240129image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.375617857
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o9167737.9%
 
N9111337.7%
 
S92743.8%
 
t92743.8%
 
e92743.8%
 
a92743.8%
 
d92743.8%
 
y92743.8%
 
U8120.3%
 
p8120.3%
 
D5640.2%
 
w5640.2%
 
n5640.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter13998757.9%
 
Uppercase Letter10176342.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N9111389.5%
 
S92749.1%
 
U8120.8%
 
D5640.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9167765.5%
 
t92746.6%
 
e92746.6%
 
a92746.6%
 
d92746.6%
 
y92746.6%
 
p8120.6%
 
w5640.4%
 
n5640.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin241750100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o9167737.9%
 
N9111337.7%
 
S92743.8%
 
t92743.8%
 
e92743.8%
 
a92743.8%
 
d92743.8%
 
y92743.8%
 
U8120.3%
 
p8120.3%
 
D5640.2%
 
w5640.2%
 
n5640.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII241750100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o9167737.9%
 
N9111337.7%
 
S92743.8%
 
t92743.8%
 
e92743.8%
 
a92743.8%
 
d92743.8%
 
y92743.8%
 
U8120.3%
 
p8120.3%
 
D5640.2%
 
w5640.2%
 
n5640.2%
 

tolbutamide
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101740 
Steady
 
23
ValueCountFrequency (%) 
No101740> 99.9%
 
Steady23< 0.1%
 
2020-09-13T20:41:37.446055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:37.592934image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:37.763136image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000904061
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10174050.0%
 
o10174050.0%
 
S23< 0.1%
 
t23< 0.1%
 
e23< 0.1%
 
a23< 0.1%
 
d23< 0.1%
 
y23< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10185550.0%
 
Uppercase Letter10176350.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101740> 99.9%
 
S23< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10174099.9%
 
t23< 0.1%
 
e23< 0.1%
 
a23< 0.1%
 
d23< 0.1%
 
y23< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203618100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10174050.0%
 
o10174050.0%
 
S23< 0.1%
 
t23< 0.1%
 
e23< 0.1%
 
a23< 0.1%
 
d23< 0.1%
 
y23< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203618100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10174050.0%
 
o10174050.0%
 
S23< 0.1%
 
t23< 0.1%
 
e23< 0.1%
 
a23< 0.1%
 
d23< 0.1%
 
y23< 0.1%
 

pioglitazone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
94436 
Steady
 
6975
Up
 
234
Down
 
118
ValueCountFrequency (%) 
No9443692.8%
 
Steady69756.9%
 
Up2340.2%
 
Down1180.1%
 
2020-09-13T20:41:37.965294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:38.083389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:38.183855image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.27648556
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o9455440.8%
 
N9443640.8%
 
S69753.0%
 
t69753.0%
 
e69753.0%
 
a69753.0%
 
d69753.0%
 
y69753.0%
 
U2340.1%
 
p2340.1%
 
D1180.1%
 
w1180.1%
 
n1180.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter12989956.1%
 
Uppercase Letter10176343.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N9443692.8%
 
S69756.9%
 
U2340.2%
 
D1180.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9455472.8%
 
t69755.4%
 
e69755.4%
 
a69755.4%
 
d69755.4%
 
y69755.4%
 
p2340.2%
 
w1180.1%
 
n1180.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin231662100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o9455440.8%
 
N9443640.8%
 
S69753.0%
 
t69753.0%
 
e69753.0%
 
a69753.0%
 
d69753.0%
 
y69753.0%
 
U2340.1%
 
p2340.1%
 
D1180.1%
 
w1180.1%
 
n1180.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII231662100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o9455440.8%
 
N9443640.8%
 
S69753.0%
 
t69753.0%
 
e69753.0%
 
a69753.0%
 
d69753.0%
 
y69753.0%
 
U2340.1%
 
p2340.1%
 
D1180.1%
 
w1180.1%
 
n1180.1%
 

rosiglitazone
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
95399 
Steady
 
6099
Up
 
178
Down
 
87
ValueCountFrequency (%) 
No9539993.7%
 
Steady60996.0%
 
Up1780.2%
 
Down870.1%
 
2020-09-13T20:41:38.350169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:38.474303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:38.581330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.241443354
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o9548641.9%
 
N9539941.8%
 
S60992.7%
 
t60992.7%
 
e60992.7%
 
a60992.7%
 
d60992.7%
 
y60992.7%
 
U1780.1%
 
p1780.1%
 
D87< 0.1%
 
w87< 0.1%
 
n87< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter12633355.4%
 
Uppercase Letter10176344.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N9539993.7%
 
S60996.0%
 
U1780.2%
 
D870.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o9548675.6%
 
t60994.8%
 
e60994.8%
 
a60994.8%
 
d60994.8%
 
y60994.8%
 
p1780.1%
 
w870.1%
 
n870.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin228096100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o9548641.9%
 
N9539941.8%
 
S60992.7%
 
t60992.7%
 
e60992.7%
 
a60992.7%
 
d60992.7%
 
y60992.7%
 
U1780.1%
 
p1780.1%
 
D87< 0.1%
 
w87< 0.1%
 
n87< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII228096100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o9548641.9%
 
N9539941.8%
 
S60992.7%
 
t60992.7%
 
e60992.7%
 
a60992.7%
 
d60992.7%
 
y60992.7%
 
U1780.1%
 
p1780.1%
 
D87< 0.1%
 
w87< 0.1%
 
n87< 0.1%
 

acarbose
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101455 
Steady
 
295
Up
 
10
Down
 
3
ValueCountFrequency (%) 
No10145599.7%
 
Steady2950.3%
 
Up10< 0.1%
 
Down3< 0.1%
 
2020-09-13T20:41:38.745446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:38.862043image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:38.973495image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.011654531
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10145849.6%
 
N10145549.6%
 
S2950.1%
 
t2950.1%
 
e2950.1%
 
a2950.1%
 
d2950.1%
 
y2950.1%
 
U10< 0.1%
 
p10< 0.1%
 
D3< 0.1%
 
w3< 0.1%
 
n3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10294950.3%
 
Uppercase Letter10176349.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10145599.7%
 
S2950.3%
 
U10< 0.1%
 
D3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10145898.6%
 
t2950.3%
 
e2950.3%
 
a2950.3%
 
d2950.3%
 
y2950.3%
 
p10< 0.1%
 
w3< 0.1%
 
n3< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin204712100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10145849.6%
 
N10145549.6%
 
S2950.1%
 
t2950.1%
 
e2950.1%
 
a2950.1%
 
d2950.1%
 
y2950.1%
 
U10< 0.1%
 
p10< 0.1%
 
D3< 0.1%
 
w3< 0.1%
 
n3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII204712100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10145849.6%
 
N10145549.6%
 
S2950.1%
 
t2950.1%
 
e2950.1%
 
a2950.1%
 
d2950.1%
 
y2950.1%
 
U10< 0.1%
 
p10< 0.1%
 
D3< 0.1%
 
w3< 0.1%
 
n3< 0.1%
 

miglitol
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101725 
Steady
 
31
Down
 
5
Up
 
2
ValueCountFrequency (%) 
No101725> 99.9%
 
Steady31< 0.1%
 
Down5< 0.1%
 
Up2< 0.1%
 
2020-09-13T20:41:39.159138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:39.292344image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:39.419118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.001316785
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10173050.0%
 
N10172549.9%
 
S31< 0.1%
 
t31< 0.1%
 
e31< 0.1%
 
a31< 0.1%
 
d31< 0.1%
 
y31< 0.1%
 
D5< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
U2< 0.1%
 
p2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10189750.0%
 
Uppercase Letter10176350.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101725> 99.9%
 
S31< 0.1%
 
D5< 0.1%
 
U2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10173099.8%
 
t31< 0.1%
 
e31< 0.1%
 
a31< 0.1%
 
d31< 0.1%
 
y31< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
p2< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203660100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10173050.0%
 
N10172549.9%
 
S31< 0.1%
 
t31< 0.1%
 
e31< 0.1%
 
a31< 0.1%
 
d31< 0.1%
 
y31< 0.1%
 
D5< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
U2< 0.1%
 
p2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203660100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10173050.0%
 
N10172549.9%
 
S31< 0.1%
 
t31< 0.1%
 
e31< 0.1%
 
a31< 0.1%
 
d31< 0.1%
 
y31< 0.1%
 
D5< 0.1%
 
w5< 0.1%
 
n5< 0.1%
 
U2< 0.1%
 
p2< 0.1%
 

troglitazone
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101760 
Steady
 
3
ValueCountFrequency (%) 
No101760> 99.9%
 
Steady3< 0.1%
 
2020-09-13T20:41:39.591290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:39.709569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:39.797039image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000117921
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176050.0%
 
o10176050.0%
 
S3< 0.1%
 
t3< 0.1%
 
e3< 0.1%
 
a3< 0.1%
 
d3< 0.1%
 
y3< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10177550.0%
 
Uppercase Letter10176350.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101760> 99.9%
 
S3< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101760> 99.9%
 
t3< 0.1%
 
e3< 0.1%
 
a3< 0.1%
 
d3< 0.1%
 
y3< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203538100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176050.0%
 
o10176050.0%
 
S3< 0.1%
 
t3< 0.1%
 
e3< 0.1%
 
a3< 0.1%
 
d3< 0.1%
 
y3< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203538100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176050.0%
 
o10176050.0%
 
S3< 0.1%
 
t3< 0.1%
 
e3< 0.1%
 
a3< 0.1%
 
d3< 0.1%
 
y3< 0.1%
 

tolazamide
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101724 
Steady
 
38
Up
 
1
ValueCountFrequency (%) 
No101724> 99.9%
 
Steady38< 0.1%
 
Up1< 0.1%
 
2020-09-13T20:41:39.973551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-13T20:41:40.096168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:40.206442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.001493667
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10172449.9%
 
o10172449.9%
 
S38< 0.1%
 
t38< 0.1%
 
e38< 0.1%
 
a38< 0.1%
 
d38< 0.1%
 
y38< 0.1%
 
U1< 0.1%
 
p1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10191550.0%
 
Uppercase Letter10176350.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101724> 99.9%
 
S38< 0.1%
 
U1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10172499.8%
 
t38< 0.1%
 
e38< 0.1%
 
a38< 0.1%
 
d38< 0.1%
 
y38< 0.1%
 
p1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203678100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10172449.9%
 
o10172449.9%
 
S38< 0.1%
 
t38< 0.1%
 
e38< 0.1%
 
a38< 0.1%
 
d38< 0.1%
 
y38< 0.1%
 
U1< 0.1%
 
p1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203678100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10172449.9%
 
o10172449.9%
 
S38< 0.1%
 
t38< 0.1%
 
e38< 0.1%
 
a38< 0.1%
 
d38< 0.1%
 
y38< 0.1%
 
U1< 0.1%
 
p1< 0.1%
 

insulin
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
47380 
Steady
30849 
Down
12218 
Up
11316 
ValueCountFrequency (%) 
No4738046.6%
 
Steady3084930.3%
 
Down1221812.0%
 
Up1131611.1%
 
2020-09-13T20:41:40.380433image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:40.520613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:40.627021image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length3.452708745
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o5959817.0%
 
N4738013.5%
 
S308498.8%
 
t308498.8%
 
e308498.8%
 
a308498.8%
 
d308498.8%
 
y308498.8%
 
D122183.5%
 
w122183.5%
 
n122183.5%
 
U113163.2%
 
p113163.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter24959571.0%
 
Uppercase Letter10176329.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N4738046.6%
 
S3084930.3%
 
D1221812.0%
 
U1131611.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o5959823.9%
 
t3084912.4%
 
e3084912.4%
 
a3084912.4%
 
d3084912.4%
 
y3084912.4%
 
w122184.9%
 
n122184.9%
 
p113164.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin351358100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o5959817.0%
 
N4738013.5%
 
S308498.8%
 
t308498.8%
 
e308498.8%
 
a308498.8%
 
d308498.8%
 
y308498.8%
 
D122183.5%
 
w122183.5%
 
n122183.5%
 
U113163.2%
 
p113163.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII351358100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o5959817.0%
 
N4738013.5%
 
S308498.8%
 
t308498.8%
 
e308498.8%
 
a308498.8%
 
d308498.8%
 
y308498.8%
 
D122183.5%
 
w122183.5%
 
n122183.5%
 
U113163.2%
 
p113163.2%
 
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101057 
Steady
 
692
Up
 
8
Down
 
6
ValueCountFrequency (%) 
No10105799.3%
 
Steady6920.7%
 
Up8< 0.1%
 
Down6< 0.1%
 
2020-09-13T20:41:40.856896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:40.973889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:41.067419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.027318377
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
o10106349.0%
 
N10105749.0%
 
S6920.3%
 
t6920.3%
 
e6920.3%
 
a6920.3%
 
d6920.3%
 
y6920.3%
 
U8< 0.1%
 
p8< 0.1%
 
D6< 0.1%
 
w6< 0.1%
 
n6< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10454350.7%
 
Uppercase Letter10176349.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N10105799.3%
 
S6920.7%
 
U8< 0.1%
 
D6< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10106396.7%
 
t6920.7%
 
e6920.7%
 
a6920.7%
 
d6920.7%
 
y6920.7%
 
p8< 0.1%
 
w6< 0.1%
 
n6< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin206306100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
o10106349.0%
 
N10105749.0%
 
S6920.3%
 
t6920.3%
 
e6920.3%
 
a6920.3%
 
d6920.3%
 
y6920.3%
 
U8< 0.1%
 
p8< 0.1%
 
D6< 0.1%
 
w6< 0.1%
 
n6< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII206306100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
o10106349.0%
 
N10105749.0%
 
S6920.3%
 
t6920.3%
 
e6920.3%
 
a6920.3%
 
d6920.3%
 
y6920.3%
 
U8< 0.1%
 
p8< 0.1%
 
D6< 0.1%
 
w6< 0.1%
 
n6< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101750 
Steady
 
13
ValueCountFrequency (%) 
No101750> 99.9%
 
Steady13< 0.1%
 
2020-09-13T20:41:41.240164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:41.381730image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:41.495761image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000510991
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10175050.0%
 
o10175050.0%
 
S13< 0.1%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10181550.0%
 
Uppercase Letter10176350.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101750> 99.9%
 
S13< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o10175099.9%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203578100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10175050.0%
 
o10175050.0%
 
S13< 0.1%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203578100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10175050.0%
 
o10175050.0%
 
S13< 0.1%
 
t13< 0.1%
 
e13< 0.1%
 
a13< 0.1%
 
d13< 0.1%
 
y13< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101762 
Steady
 
1
ValueCountFrequency (%) 
No101762> 99.9%
 
Steady1< 0.1%
 
2020-09-13T20:41:41.687676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-13T20:41:41.774959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:41.871464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000039307
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176250.0%
 
o10176250.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10176750.0%
 
Uppercase Letter10176350.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101762> 99.9%
 
S1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101762> 99.9%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203530100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176250.0%
 
o10176250.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203530100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176250.0%
 
o10176250.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101761 
Steady
 
2
ValueCountFrequency (%) 
No101761> 99.9%
 
Steady2< 0.1%
 
2020-09-13T20:41:42.012224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:42.146282image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:42.252946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000078614
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176150.0%
 
o10176150.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10177150.0%
 
Uppercase Letter10176350.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101761> 99.9%
 
S2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101761> 99.9%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203534100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176150.0%
 
o10176150.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203534100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176150.0%
 
o10176150.0%
 
S2< 0.1%
 
t2< 0.1%
 
e2< 0.1%
 
a2< 0.1%
 
d2< 0.1%
 
y2< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
No
101762 
Steady
 
1
ValueCountFrequency (%) 
No101762> 99.9%
 
Steady1< 0.1%
 
2020-09-13T20:41:42.430732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-13T20:41:42.543578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:42.620368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length2
Mean length2.000039307
Min length2

Overview of Unicode Properties

Unique unicode characters8
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N10176250.0%
 
o10176250.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10176750.0%
 
Uppercase Letter10176350.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N101762> 99.9%
 
S1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
o101762> 99.9%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin203530100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N10176250.0%
 
o10176250.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203530100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N10176250.0%
 
o10176250.0%
 
S1< 0.1%
 
t1< 0.1%
 
e1< 0.1%
 
a1< 0.1%
 
d1< 0.1%
 
y1< 0.1%
 

change
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
0
54754 
1
47009 
ValueCountFrequency (%) 
05475453.8%
 
14700946.2%
 
2020-09-13T20:41:42.739982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
1
78361 
0
23402 
ValueCountFrequency (%) 
17836177.0%
 
02340223.0%
 
2020-09-13T20:41:42.780093image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

readmitted
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size795.1 KiB
NO
54861 
>30
35545 
<30
11357 
ValueCountFrequency (%) 
NO5486153.9%
 
>303554534.9%
 
<301135711.2%
 
2020-09-13T20:41:42.847426image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:42.954272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:43.031062image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.460894431
Min length2

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N5486121.9%
 
O5486121.9%
 
34690218.7%
 
04690218.7%
 
>3554514.2%
 
<113574.5%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter10972243.8%
 
Decimal Number9380437.5%
 
Math Symbol4690218.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N5486150.0%
 
O5486150.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
>3554575.8%
 
<1135724.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
34690250.0%
 
04690250.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common14070656.2%
 
Latin10972243.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N5486150.0%
 
O5486150.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
34690233.3%
 
04690233.3%
 
>3554525.3%
 
<113578.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII250428100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N5486121.9%
 
O5486121.9%
 
34690218.7%
 
04690218.7%
 
>3554514.2%
 
<113574.5%
 

cat_diag_1
Categorical

Distinct18
Distinct (%)< 0.1%
Missing21
Missing (%)< 0.1%
Memory size795.1 KiB
07
30335 
03
11459 
08
10407 
09
9208 
16
7636 
Other values (13)
32697 
ValueCountFrequency (%) 
073033529.8%
 
031145911.3%
 
081040710.2%
 
0992089.0%
 
1676367.5%
 
1769726.9%
 
1050785.0%
 
1349574.9%
 
0234333.4%
 
0127682.7%
 
1225302.5%
 
0522622.2%
 
1816441.6%
 
0612111.2%
 
0411031.1%
 
116870.7%
 
14510.1%
 
191< 0.1%
 
(Missing)21< 0.1%
 
2020-09-13T20:41:43.183935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-09-13T20:41:43.351869image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.000206362
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
07726438.0%
 
73730718.3%
 
13301116.2%
 
3164168.1%
 
8120515.9%
 
992094.5%
 
688474.3%
 
259632.9%
 
522621.1%
 
411540.6%
 
n42< 0.1%
 
a21< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number203484> 99.9%
 
Lowercase Letter63< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
07726438.0%
 
73730718.3%
 
13301116.2%
 
3164168.1%
 
8120515.9%
 
992094.5%
 
688474.3%
 
259632.9%
 
522621.1%
 
411540.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n4266.7%
 
a2133.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common203484> 99.9%
 
Latin63< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
07726438.0%
 
73730718.3%
 
13301116.2%
 
3164168.1%
 
8120515.9%
 
992094.5%
 
688474.3%
 
259632.9%
 
522621.1%
 
411540.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n4266.7%
 
a2133.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203547100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
07726438.0%
 
73730718.3%
 
13301116.2%
 
3164168.1%
 
8120515.9%
 
992094.5%
 
688474.3%
 
259632.9%
 
522621.1%
 
411540.6%
 
n42< 0.1%
 
a21< 0.1%
 

cat_diag_2
Categorical

Distinct18
Distinct (%)< 0.1%
Missing358
Missing (%)0.4%
Memory size795.1 KiB
07
31364 
03
21017 
08
10251 
10
7987 
16
4632 
Other values (13)
26154 
ValueCountFrequency (%) 
073136430.8%
 
032101720.7%
 
081025110.1%
 
1079877.8%
 
1646324.6%
 
0939623.9%
 
1235963.5%
 
0429262.9%
 
0526572.6%
 
0225472.5%
 
1724262.4%
 
0119311.9%
 
1818051.8%
 
1317641.7%
 
0612861.3%
 
197310.7%
 
114150.4%
 
141080.1%
 
(Missing)3580.4%
 
2020-09-13T20:41:43.537328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:43.711051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.003517978
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
08592842.1%
 
73379016.6%
 
12581012.7%
 
32278111.2%
 
8120565.9%
 
261433.0%
 
659182.9%
 
946932.3%
 
430341.5%
 
526571.3%
 
n7160.4%
 
a3580.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number20281099.5%
 
Lowercase Letter10740.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n71666.7%
 
a35833.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
08592842.4%
 
73379016.7%
 
12581012.7%
 
32278111.2%
 
8120565.9%
 
261433.0%
 
659182.9%
 
946932.3%
 
430341.5%
 
526571.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common20281099.5%
 
Latin10740.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n71666.7%
 
a35833.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
08592842.4%
 
73379016.7%
 
12581012.7%
 
32278111.2%
 
8120565.9%
 
261433.0%
 
659182.9%
 
946932.3%
 
430341.5%
 
526571.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII203884100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
08592842.1%
 
73379016.6%
 
12581012.7%
 
32278111.2%
 
8120565.9%
 
261433.0%
 
659182.9%
 
946932.3%
 
430341.5%
 
526571.3%
 
n7160.4%
 
a3580.2%
 

cat_diag_3
Categorical

MISSING

Distinct18
Distinct (%)< 0.1%
Missing1423
Missing (%)1.4%
Memory size795.1 KiB
07
29917 
03
26308 
08
6774 
10
6327 
16
4523 
Other values (13)
26491 
ValueCountFrequency (%) 
072991729.4%
 
032630825.9%
 
0867746.7%
 
1063276.2%
 
1645234.4%
 
1838143.7%
 
0935723.5%
 
0531363.1%
 
0424902.4%
 
1224882.4%
 
1719451.9%
 
1319151.9%
 
0118611.8%
 
0218561.8%
 
0617661.7%
 
1912431.2%
 
113090.3%
 
14960.1%
 
(Missing)14231.4%
 
2020-09-13T20:41:43.895020image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-13T20:41:44.079801image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.013983471
Min length2

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
08400741.0%
 
73186215.5%
 
32822313.8%
 
12483012.1%
 
8105885.2%
 
662893.1%
 
948152.3%
 
243442.1%
 
531361.5%
 
n28461.4%
 
425861.3%
 
a14230.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number20068097.9%
 
Lowercase Letter42692.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n284666.7%
 
a142333.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
08400741.9%
 
73186215.9%
 
32822314.1%
 
12483012.4%
 
8105885.3%
 
662893.1%
 
948152.4%
 
243442.2%
 
531361.6%
 
425861.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common20068097.9%
 
Latin42692.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n284666.7%
 
a142333.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
08400741.9%
 
73186215.9%
 
32822314.1%
 
12483012.4%
 
8105885.3%
 
662893.1%
 
948152.4%
 
243442.2%
 
531361.6%
 
425861.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII204949100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
08400741.0%
 
73186215.5%
 
32822313.8%
 
12483012.1%
 
8105885.2%
 
662893.1%
 
948152.3%
 
243442.1%
 
531361.5%
 
n28461.4%
 
425861.3%
 
a14230.7%
 

Interactions

2020-09-13T20:41:01.879813image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:01.995578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:02.141284image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:02.319504image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:02.486786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:02.605693image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:02.746524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:02.884744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:03.007436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:03.122187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:03.259465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:03.375688image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:03.493299image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:03.633724image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:03.772308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:03.916142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:04.069543image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:04.233893image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:04.405625image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:04.603467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:04.756876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:04.911937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:05.031958image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:05.213799image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:05.348593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:05.477123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:05.636810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:05.790054image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:05.946917image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:06.236895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:06.360817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:06.492889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:06.677256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:06.797560image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:06.922705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:07.043717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:07.164068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:07.276532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:07.393723image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:07.513535image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:07.623981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:07.730614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:07.842296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:07.948891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:08.058034image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:08.175569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:08.285095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:08.402681image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:08.516909image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:08.629876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:08.734424image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:08.845293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:08.956541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:09.064629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:09.179931image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:09.293656image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:09.401224image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:09.509990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:09.627317image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:09.734437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:09.854363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:09.980980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:10.166019image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:10.318335image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:10.437608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:10.555087image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:10.678981image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:10.800742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:10.923580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:11.171872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:11.295993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:11.426376image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:11.543742image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:11.671545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:11.802684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:11.928766image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:12.045267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:12.168423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:12.294462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:12.401532image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:12.506359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:12.615294image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:12.719820image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:12.823765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:12.937842image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:13.040275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:13.155671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:13.268329image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:13.380305image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:13.481972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:13.589262image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:13.697221image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:13.817771image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:13.936654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:14.060683image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:14.179642image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:14.303930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:14.431863image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:14.549943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:14.677919image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:14.803328image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:14.927780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:15.044957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:15.168701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:15.295326image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:15.413132image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:15.531674image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:15.651623image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:15.766346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:15.881157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:16.005162image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:16.119446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:16.245399image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:16.367213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:16.487887image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:16.600785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:16.720580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:17.001313image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:17.118537image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:17.233834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:17.358050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:17.472784image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:17.589601image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:17.715010image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:17.827622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:17.952343image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:18.073350image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:18.194756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:18.308182image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:18.427008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:18.551142image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:18.655175image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:18.758096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:18.866559image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:18.970095image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:19.074971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:19.189591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:19.292161image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:19.404583image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:19.512907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:19.621891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:19.722240image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:19.829705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:19.937146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:20.051481image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:20.168561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:20.290336image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:20.401409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:20.514629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:20.635800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:20.744847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:20.865965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:20.982895image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:21.100461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:21.207331image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:21.323046image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:21.449667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:21.566030image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:21.677805image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:21.795228image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:21.909218image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:22.024152image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:22.150593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:22.264255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:22.387914image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:22.508567image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:22.627502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:22.737943image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:22.854986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-13T20:41:44.259402image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-13T20:41:44.525971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-13T20:41:44.760906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-13T20:41:45.018138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-13T20:41:45.556867image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-13T20:41:23.640972image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:25.581727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:26.382593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-13T20:41:26.711743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

df_indexracegenderageadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalmedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideinsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmittedcat_diag_1cat_diag_2cat_diag_3
00CaucasianFemale1.79175962510.693147Pediatrics-Endocrinology3.7376700.0000000.6931470.0000000.00.0000001NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNo00NO03NaNNaN
11CaucasianFemale2.7725891171.386294NaN4.0943450.0000002.9444390.0000000.00.0000009NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNo11>30030303
22AfricanAmericanFemale3.2580971171.098612NaN2.4849071.7917592.6390571.0986120.00.6931476NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNo01NO110318
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44CaucasianMale3.8286411170.693147NaN3.9512440.0000002.1972250.0000000.00.0000005NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoSteadyNoNoNoNoNo11NO020203
55CaucasianMale4.0253522121.386294NaN3.4657361.9459102.8332130.0000000.00.0000009NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNo01>30070703
66CaucasianMale4.1896553121.609438NaN4.2626800.6931473.0910420.0000000.00.0000007NoneNoneSteadyNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNo11NO070718
77CaucasianMale4.3307331171.791759NaN4.3040650.0000002.5649490.0000000.00.0000008NoneNoneNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNo01>30070803
88CaucasianFemale4.4543472142.639057NaN4.2341071.0986123.3672960.0000000.00.0000008NoneNoneNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoSteadyNoNoNoNoNo11NO070701
99CaucasianFemale4.5643483342.564949InternalMedicine3.5263611.3862942.9444390.0000000.00.0000008NoneNoneNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoSteadyNoNoNoNoNo11NO070208

Last rows

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101754101757CaucasianFemale4.3307331171.791759NaN3.0910420.6931472.8332130.0000000.0000000.6931479NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNo01NO080808
101755101758CaucasianFemale4.4543471171.791759NaN4.3438050.6931473.1354940.0000000.6931470.0000009NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNo11NO050105
101756101759CaucasianMale4.4543471170.693147NaN0.6931470.0000002.7725891.3862940.0000000.0000007NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNo11NO071603
101757101760AfricanAmericanFemale4.1896551171.945910NaN3.8286410.6931473.2580971.3862940.6931471.0986129NoneNoneNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoDownNoNoNoNoNo11>30060707
101758101761AfricanAmericanMale4.3307331371.386294NaN3.9512440.0000002.8332130.0000000.0000000.0000009None>8SteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNo11>30030507
101759101762AfricanAmericanFemale4.4543471451.791759NaN3.5263611.3862942.9444390.0000000.0000000.6931479NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNo01NO090316
101760101763CaucasianMale4.3307331170.693147NaN3.9889840.0000002.3025850.6931470.0000000.00000013NoneNoneSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNo11NO011005
101761101764CaucasianFemale4.4543472372.397895Surgery-General3.8286411.0986123.0910420.0000000.0000000.6931479NoneNoneNoNoNoNoNoNoSteadyNoNoSteadyNoNoNoNoNoUpNoNoNoNoNo11NO170417
101762101765CaucasianMale4.3307331171.945910NaN2.6390571.3862941.3862940.0000000.0000000.0000009NoneNoneNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNo00NO090916